Nanosignal Processing:  Stochastic Resonance in Carbon Nanotubes That Detect Subthreshold Signals

Abstract

Experiments confirm that small amounts of noise help a nanotube transistor detect noisy subthreshold electrical signals. Gaussian, uniform, and impulsive (Cauchy) noise produced this feedforward stochastic-resonance effect by increasing both the nanotube system's mutual information and its input−output correlation. The noise corrupted a synchronous Bernoulli or random digital sequence that fed into the thresholdlike nanotube transistor and produced a Bernoulli sequence. Both Shannon's mutual information and correlation measured the performance gain by comparing the input and output sequences. This nanotube SR effect was robust: it persisted even when infinite-variance Cauchy noise corrupted the signal stream. Such noise-enhanced signal processing at the nanolevel promises applications to signal detection in wideband communication systems and biological and artificial neural networks.

Desigining Circuits from Imperfect Components in VISI Giga-scale Technologies

“…This phenomenon was discovered in the 80's [31] and an extensive theoretical research has been realized since then (for an overview see [32] ). This phenomenon has been observed in biological systems [33] , but has been also used for engineering applications [34,35] . A second phenomenon, more recently observed, is suprathreshold stochastic resonance (SSR) [36,37] .…”

Artificial intelligence in nanotechnology

“…For example, some properties of CNT allow us to design nanotube detectors that might help implement the behavior of pulse-train neural networks because the detectors are threshold devices similar to spiking neurons. [101] The detection of volatile organic compounds by carbon nanotube coated acoustic and optical sensors has also been successfully studied with the help of ANNs. [102] As in many other studies, a principal component analysis treatment was previously performed to improve the ANN classification efficiency.…”

Predictive dynamical and stochastic systems

“…The main motivation here stems from the fact that a combination of prediction dynamics and noise could lead to behaviors similar to "stochastic resonance" [8,9,10]. Stochastic resonance has been studied in variety of fields [11,12,13,14,15]. In particular, we have proposed a variation of stochastic resonance, called delayed stochastic resonance, which arises from a combination of noise with delayed dynamics [16,17,18].…”